Integration of Product Lifecycle Behavior into Component Design, Manufacturing and Performance Analysis to Realize a Digital Twin Representation Through a Model-Based Feature Information Network

Saikiran Gopalakrishnan, Purdue University

Abstract

There has been a growing interest within the aerospace industry for shifting towards a digital twin approach, for reliable assessment of individual components during the product lifecycle - across design, manufacturing, and in-service maintenance, repair & overhaul (MRO) stages. The transition towards digital twins relies on continuous updating of the product lifecycle datasets and interoperable exchange of data applicable to components, thereby permitting engineers to utilize current state information to make more-informed downstream decisions. In this thesis, we primarily develop a framework to store, track, update, and retrieve product lifecycle data applicable to a serialized component, its features, and individual locations. From a structural integrity standpoint, the fatigue performance of a component is inherently tied to the component geometry, its material state, and applied loading conditions. The manufacturing process controls the underlying material microstructure, which in turn governs the mechanical properties and ultimately the performance. The processing also controls the residual stress distributions within the component volume, which influences the durability and damage tolerance of the component. Hence, we have demonstrated multiple use cases for fatigue life assessment of critical aerospace components, by using the developed framework for efficiently tracking and retrieving (i) the current geometric state, (ii) the material microstructure state, and (iii) residual stress distributions. Model-based definitions (MBDs) present opportunities to capture both geometric and non-geometric data using 3D computer-aided design (CAD) models, with the overarching aim to disseminate product information across different stages of the lifecycle. MBDs can potentially eliminate error-prone information exchange associated with traditional paper-based drawings and improve the fidelity of component details, captured using 3D CAD models. However, current CAD capabilities limit associating the material information with the component’s shape definition. Furthermore, the material attributes of interest, viz., material microstructures and residual stress distributions, can vary across the component volume. To this end, in the first part of the thesis, we implement a CAD-based tool to store and retrieve metadata using point objects within a CAD model, thereby creating associations to spatial locations within the component. The tool is illustrated for storage and retrieval of bulk residual stresses developed during the manufacturing of a turbine disk component, acquired from process modeling and characterization. Further, variations in residual stress distribution owing to process model uncertainties have been captured as separate instances of the disk’s CAD models to represent part-to-part variability as an analogy to track individual serialized components for digital twins. The propagation of varying residual stresses from these CAD models within the damage tolerance analysis performed at critical locations in the disk has been demonstrated. The combination of geometric and non-geometric data inside the MBD, via storage of spatial and feature varying information, presents opportunities to create digital replica or digital twin(s) of actual component(s) with location-specific material state information. To fully realize a digital twin description of components, it is crucial to dynamically update information tied to a component as it evolves across the lifecycle, and subsequently track and retrieve current state information. Hence, in the second part of the thesis, we propose a dynamic data linking approach to include material information within the MBDs. As opposed to storing material datasets directly within the CAD model in the previous approach, we externally store and update the material datasets and create data linkages between material datasets and features within the CAD models.

Degree

Ph.D.

Advisors

Sangid, Purdue University.

Subject Area

Design|Physics|Aerospace engineering|Computer Engineering|Computer science|Industrial engineering|Mathematics|Mechanics

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